以計算智慧為基礎之新的避險比例決定方法

Abstract

本研究提出了一個整合計算智慧與統計方法學的最適避險比例決定方法，用來改善不同避險區間下最小變異避險比例之預測準確度。透過衡量金融市場現貨及期貨商品報酬時間序列之變異數、共變數、價差及其他們的一階、二階變量，市場波動的動態行為可以被擷取出來，之後以增長階層式自我組織圖進行階層式的分群。經過分群，這些位在相同集群裡具有相似行為的時間序列資料，經過給予不同的權重進行重新取樣後，會被蒐集起來用來取代原先估算最適避險比例的資料樣本。我們將這個方法運用在台灣加權股價指數、標準普爾500指數、金融時報100指數、以及日經255指數之避險實證研究上，對於避險區間之長短與避險效果的關係進行研究。實驗結果顯示，這個方法所估算之避險比例，在中、長期避險區間下可以顯著地得到優於傳統最小平方法模型及天真避險模型之表現，決定出各種避險期間下之最適避險比例。

In this study, a novel procedure combining computational intelligence and statistical methodologies is proposed to improve the accuracy of minimum -variance optimal hedge ratio (OHR) estimation over various hedging horizons. The time series of financial asset returns are clustered hierarchically using a growing hierarchical self-organizing map (GHSOM) based on the dynamic behaviors of market fluctuation extracted by measurement of variances, covariance, price spread, and their first and second differences. Instead of using original observations, observations with similar patterns in the same cluster and weighted by a resample process are collected to estimate the OHR. Four stock market indexes and related futures contracts, including Taiwan Weighted Index (TWI), Standard & Poor's 500 Index (S&P 500), Financial Times Stock Exchange 100 Index (FTSE 100), and NIKKEI 255 Index, are adopted in empirical experiments to investigate the correlation between hedging horizon and performance. Results of the experiments demonstrate that the proposed approach can significantly improve OHR decisions for mid-term and long-term hedging compared with traditional ordinary least squares and naïve models.